Zhang et al. proposed in [1]} the Scale-adaptive CNN (SaCNN) architecture, another deep single column cnn for density estimation. In this case, the spatial resolution has been preserved by small fixed-sized filters. Furthermore, the authors introduced a multi-task loss by adding a relative head-count loss to the density map loss that significantly improves the network generalization in crowd scenes with few pedestrians, where most representative works perform poorly. More, similarly to [2]}, Chen et al. in [3]} proposed the Scale Pyramid Network (SPN) which adopts a shared single deep column structure and extracts multi-scale information in high layers by Scale Pyramid Module. In particular, in the Scale Pyramid Module, they specifically employed different rates of dilated convolutions in parallel instead of traditional convolutions with different sizes.
